Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations240
Missing cells22
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.9 KiB
Average record size in memory144.6 B

Variable types

Categorical4
Numeric14

Alerts

Beverage is highly overall correlated with Beverage_category and 2 other fieldsHigh correlation
Beverage_category is highly overall correlated with BeverageHigh correlation
Beverage_prep is highly overall correlated with Saturated Fat (g)High correlation
Calcium (% DV) is highly overall correlated with Protein (g) and 3 other fieldsHigh correlation
Calories is highly overall correlated with Cholesterol (mg) and 5 other fieldsHigh correlation
Cholesterol (mg) is highly overall correlated with Calories and 2 other fieldsHigh correlation
Dietary Fibre (g) is highly overall correlated with Beverage and 1 other fieldsHigh correlation
Iron (% DV) is highly overall correlated with Dietary Fibre (g) and 1 other fieldsHigh correlation
Protein (g) is highly overall correlated with Calcium (% DV) and 5 other fieldsHigh correlation
Saturated Fat (g) is highly overall correlated with Beverage_prep and 2 other fieldsHigh correlation
Sodium (mg) is highly overall correlated with Protein (g) and 3 other fieldsHigh correlation
Sugars (g) is highly overall correlated with Calories and 2 other fieldsHigh correlation
Total Carbohydrates (g) is highly overall correlated with Calories and 3 other fieldsHigh correlation
Total Fat (g) is highly overall correlated with Calcium (% DV) and 4 other fieldsHigh correlation
Trans Fat (g) is highly overall correlated with Calcium (% DV) and 6 other fieldsHigh correlation
Vitamin A (% DV) is highly overall correlated with Calcium (% DV) and 2 other fieldsHigh correlation
Vitamin C (% DV) is highly overall correlated with BeverageHigh correlation
Caffeine (mg) has 22 (9.2%) missing values Missing
Calories has 4 (1.7%) zeros Zeros
Total Fat (g) has 21 (8.8%) zeros Zeros
Trans Fat (g) has 33 (13.8%) zeros Zeros
Sodium (mg) has 111 (46.2%) zeros Zeros
Total Carbohydrates (g) has 11 (4.6%) zeros Zeros
Cholesterol (mg) has 8 (3.3%) zeros Zeros
Dietary Fibre (g) has 140 (58.3%) zeros Zeros
Sugars (g) has 14 (5.8%) zeros Zeros
Protein (g) has 11 (4.6%) zeros Zeros
Vitamin A (% DV) has 27 (11.2%) zeros Zeros
Vitamin C (% DV) has 187 (77.9%) zeros Zeros
Calcium (% DV) has 23 (9.6%) zeros Zeros
Iron (% DV) has 107 (44.6%) zeros Zeros
Caffeine (mg) has 34 (14.2%) zeros Zeros

Reproduction

Analysis started2025-01-20 00:54:40.484299
Analysis finished2025-01-20 00:54:47.305075
Duration6.82 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Beverage_category
Categorical

High correlation 

Distinct9
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Classic Espresso Drinks
58 
Tazo® Tea Drinks
52 
Signature Espresso Drinks
40 
Frappuccino® Blended Coffee
36 
Shaken Iced Beverages
17 
Other values (4)
37 

Length

Max length33
Median length26
Mean length22.116667
Min length6

Characters and Unicode

Total characters5308
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoffee
2nd rowCoffee
3rd rowCoffee
4th rowCoffee
5th rowClassic Espresso Drinks

Common Values

ValueCountFrequency (%)
Classic Espresso Drinks 58
24.2%
Tazo® Tea Drinks 52
21.7%
Signature Espresso Drinks 40
16.7%
Frappuccino® Blended Coffee 36
15.0%
Shaken Iced Beverages 17
 
7.1%
Frappuccino® Light Blended Coffee 12
 
5.0%
Frappuccino® Blended Crème 12
 
5.0%
Smoothies 9
 
3.8%
Coffee 4
 
1.7%

Length

2025-01-19T19:54:47.333562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:54:47.376814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
drinks 150
21.2%
espresso 98
13.9%
frappuccino® 60
 
8.5%
blended 60
 
8.5%
classic 58
 
8.2%
tazo® 52
 
7.4%
tea 52
 
7.4%
coffee 52
 
7.4%
signature 40
 
5.7%
shaken 17
 
2.4%
Other values (5) 67
9.5%

Most occurring characters

ValueCountFrequency (%)
s 586
 
11.0%
e 520
 
9.8%
466
 
8.8%
r 377
 
7.1%
i 329
 
6.2%
n 327
 
6.2%
a 296
 
5.6%
o 280
 
5.3%
p 218
 
4.1%
c 195
 
3.7%
Other values (22) 1714
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4024
75.8%
Uppercase Letter 706
 
13.3%
Space Separator 466
 
8.8%
Other Symbol 112
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 586
14.6%
e 520
12.9%
r 377
9.4%
i 329
8.2%
n 327
8.1%
a 296
7.4%
o 280
 
7.0%
p 218
 
5.4%
c 195
 
4.8%
k 167
 
4.2%
Other values (11) 729
18.1%
Uppercase Letter
ValueCountFrequency (%)
D 150
21.2%
C 122
17.3%
T 104
14.7%
E 98
13.9%
B 77
10.9%
S 66
9.3%
F 60
 
8.5%
I 17
 
2.4%
L 12
 
1.7%
Space Separator
ValueCountFrequency (%)
466
100.0%
Other Symbol
ValueCountFrequency (%)
® 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4730
89.1%
Common 578
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 586
12.4%
e 520
 
11.0%
r 377
 
8.0%
i 329
 
7.0%
n 327
 
6.9%
a 296
 
6.3%
o 280
 
5.9%
p 218
 
4.6%
c 195
 
4.1%
k 167
 
3.5%
Other values (20) 1435
30.3%
Common
ValueCountFrequency (%)
466
80.6%
® 112
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5184
97.7%
None 124
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 586
 
11.3%
e 520
 
10.0%
466
 
9.0%
r 377
 
7.3%
i 329
 
6.3%
n 327
 
6.3%
a 296
 
5.7%
o 280
 
5.4%
p 218
 
4.2%
c 195
 
3.8%
Other values (20) 1590
30.7%
None
ValueCountFrequency (%)
® 112
90.3%
è 12
 
9.7%

Beverage
Categorical

High correlation 

Distinct33
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Tazo® Full-Leaf Red Tea Latte (Vanilla Rooibos)
 
12
White Chocolate Mocha (Without Whipped Cream)
 
12
Tazo® Full-Leaf Tea Latte
 
12
Tazo® Green Tea Latte
 
12
Tazo® Chai Tea Latte
 
12
Other values (28)
180 

Length

Max length51
Median length37
Mean length27.520833
Min length5

Characters and Unicode

Total characters6605
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrewed Coffee
2nd rowBrewed Coffee
3rd rowBrewed Coffee
4th rowBrewed Coffee
5th rowCaffè Latte

Common Values

ValueCountFrequency (%)
Tazo® Full-Leaf Red Tea Latte (Vanilla Rooibos) 12
 
5.0%
White Chocolate Mocha (Without Whipped Cream) 12
 
5.0%
Tazo® Full-Leaf Tea Latte 12
 
5.0%
Tazo® Green Tea Latte 12
 
5.0%
Tazo® Chai Tea Latte 12
 
5.0%
Coffee 12
 
5.0%
Hot Chocolate (Without Whipped Cream) 12
 
5.0%
Caramel Macchiato 12
 
5.0%
Cappuccino 12
 
5.0%
Vanilla Latte (Or Other Flavoured Latte) 12
 
5.0%
Other values (23) 120
50.0%

Length

2025-01-19T19:54:47.436716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
latte 88
 
8.9%
without 79
 
8.0%
cream 79
 
8.0%
whipped 79
 
8.0%
tazo® 58
 
5.9%
tea 58
 
5.9%
mocha 36
 
3.7%
vanilla 28
 
2.8%
caramel 28
 
2.8%
caffè 28
 
2.8%
Other values (40) 423
43.0%

Most occurring characters

ValueCountFrequency (%)
744
 
11.3%
a 676
 
10.2%
e 625
 
9.5%
t 446
 
6.8%
o 372
 
5.6%
i 329
 
5.0%
h 313
 
4.7%
C 250
 
3.8%
r 241
 
3.6%
p 225
 
3.4%
Other values (37) 2384
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4523
68.5%
Uppercase Letter 992
 
15.0%
Space Separator 744
 
11.3%
Open Punctuation 124
 
1.9%
Close Punctuation 124
 
1.9%
Other Symbol 58
 
0.9%
Dash Punctuation 24
 
0.4%
Other Punctuation 16
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 676
14.9%
e 625
13.8%
t 446
9.9%
o 372
 
8.2%
i 329
 
7.3%
h 313
 
6.9%
r 241
 
5.3%
p 225
 
5.0%
l 204
 
4.5%
c 153
 
3.4%
Other values (14) 939
20.8%
Uppercase Letter
ValueCountFrequency (%)
C 250
25.2%
W 187
18.9%
T 116
11.7%
L 115
11.6%
M 59
 
5.9%
S 51
 
5.1%
F 40
 
4.0%
V 28
 
2.8%
B 28
 
2.8%
O 27
 
2.7%
Other values (7) 91
 
9.2%
Space Separator
ValueCountFrequency (%)
744
100.0%
Open Punctuation
ValueCountFrequency (%)
( 124
100.0%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Other Symbol
ValueCountFrequency (%)
® 58
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Other Punctuation
ValueCountFrequency (%)
& 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5515
83.5%
Common 1090
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 676
 
12.3%
e 625
 
11.3%
t 446
 
8.1%
o 372
 
6.7%
i 329
 
6.0%
h 313
 
5.7%
C 250
 
4.5%
r 241
 
4.4%
p 225
 
4.1%
l 204
 
3.7%
Other values (31) 1834
33.3%
Common
ValueCountFrequency (%)
744
68.3%
( 124
 
11.4%
) 124
 
11.4%
® 58
 
5.3%
- 24
 
2.2%
& 16
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6511
98.6%
None 94
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
744
 
11.4%
a 676
 
10.4%
e 625
 
9.6%
t 446
 
6.8%
o 372
 
5.7%
i 329
 
5.1%
h 313
 
4.8%
C 250
 
3.8%
r 241
 
3.7%
p 225
 
3.5%
Other values (35) 2290
35.2%
None
ValueCountFrequency (%)
® 58
61.7%
è 36
38.3%

Beverage_prep
Categorical

High correlation 

Distinct13
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Soymilk
65 
2 Milk
49 
Grande Nonfat Milk
26 
Tall Nonfat Milk
23 
Venti Nonfat Milk
22 
Other values (8)
55 

Length

Max length18
Median length17
Mean length10.241667
Min length4

Characters and Unicode

Total characters2458
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowShort
2nd rowTall
3rd rowGrande
4th rowVenti
5th rowShort Nonfat Milk

Common Values

ValueCountFrequency (%)
Soymilk 65
27.1%
2 Milk 49
20.4%
Grande Nonfat Milk 26
 
10.8%
Tall Nonfat Milk 23
 
9.6%
Venti Nonfat Milk 22
 
9.2%
Whole Milk 16
 
6.7%
Short Nonfat Milk 12
 
5.0%
Tall 7
 
2.9%
Grande 7
 
2.9%
Venti 7
 
2.9%
Other values (3) 6
 
2.5%

Length

2025-01-19T19:54:47.483051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
milk 148
31.4%
nonfat 83
17.6%
soymilk 65
13.8%
2 49
 
10.4%
grande 33
 
7.0%
tall 30
 
6.4%
venti 29
 
6.2%
whole 16
 
3.4%
short 16
 
3.4%
solo 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 290
11.8%
i 243
 
9.9%
231
 
9.4%
k 213
 
8.7%
o 184
 
7.5%
M 148
 
6.0%
a 146
 
5.9%
n 145
 
5.9%
t 128
 
5.2%
f 83
 
3.4%
Other values (15) 647
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1756
71.4%
Uppercase Letter 422
 
17.2%
Space Separator 231
 
9.4%
Decimal Number 49
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 290
16.5%
i 243
13.8%
k 213
12.1%
o 184
10.5%
a 146
8.3%
n 145
8.3%
t 128
7.3%
f 83
 
4.7%
e 78
 
4.4%
m 65
 
3.7%
Other values (5) 181
10.3%
Uppercase Letter
ValueCountFrequency (%)
M 148
35.1%
N 83
19.7%
S 82
19.4%
G 33
 
7.8%
T 30
 
7.1%
V 29
 
6.9%
W 16
 
3.8%
D 1
 
0.2%
Space Separator
ValueCountFrequency (%)
231
100.0%
Decimal Number
ValueCountFrequency (%)
2 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2178
88.6%
Common 280
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 290
13.3%
i 243
11.2%
k 213
9.8%
o 184
 
8.4%
M 148
 
6.8%
a 146
 
6.7%
n 145
 
6.7%
t 128
 
5.9%
f 83
 
3.8%
N 83
 
3.8%
Other values (13) 515
23.6%
Common
ValueCountFrequency (%)
231
82.5%
2 49
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 290
11.8%
i 243
 
9.9%
231
 
9.4%
k 213
 
8.7%
o 184
 
7.5%
M 148
 
6.0%
a 146
 
5.9%
n 145
 
5.9%
t 128
 
5.2%
f 83
 
3.4%
Other values (15) 647
26.3%

Calories
Real number (ℝ)

High correlation  Zeros 

Distinct48
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.77917
Minimum0
Maximum510
Zeros4
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:47.529686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.75
Q1120
median185
Q3260
95-th percentile370
Maximum510
Range510
Interquartile range (IQR)140

Descriptive statistics

Standard deviation102.75061
Coefficient of variation (CV)0.5302459
Kurtosis-0.056887261
Mean193.77917
Median Absolute Deviation (MAD)75
Skewness0.38213101
Sum46507
Variance10557.687
MonotonicityNot monotonic
2025-01-19T19:54:47.579716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
150 11
 
4.6%
190 11
 
4.6%
180 11
 
4.6%
120 10
 
4.2%
100 10
 
4.2%
130 10
 
4.2%
200 10
 
4.2%
240 9
 
3.8%
80 9
 
3.8%
110 9
 
3.8%
Other values (38) 140
58.3%
ValueCountFrequency (%)
0 4
1.7%
3 1
 
0.4%
4 1
 
0.4%
5 4
1.7%
10 2
0.8%
15 1
 
0.4%
25 1
 
0.4%
50 2
0.8%
60 4
1.7%
70 3
1.2%
ValueCountFrequency (%)
510 1
 
0.4%
460 2
0.8%
450 2
0.8%
430 1
 
0.4%
420 1
 
0.4%
400 1
 
0.4%
390 2
0.8%
380 1
 
0.4%
370 3
1.2%
360 1
 
0.4%

Total Fat (g)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9116667
Minimum0
Maximum15
Zeros21
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:47.620007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median2.5
Q34.5
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation2.9540323
Coefficient of variation (CV)1.0145503
Kurtosis1.1227851
Mean2.9116667
Median Absolute Deviation (MAD)2.3
Skewness1.1404752
Sum698.8
Variance8.7263068
MonotonicityNot monotonic
2025-01-19T19:54:47.662788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.1 34
14.2%
0 21
 
8.8%
1.5 16
 
6.7%
3 15
 
6.2%
5 15
 
6.2%
0.2 14
 
5.8%
4 14
 
5.8%
2.5 13
 
5.4%
6 13
 
5.4%
3.5 12
 
5.0%
Other values (13) 73
30.4%
ValueCountFrequency (%)
0 21
8.8%
0.1 34
14.2%
0.2 14
5.8%
0.3 6
 
2.5%
0.4 2
 
0.8%
0.5 4
 
1.7%
1 12
 
5.0%
1.5 16
6.7%
2 10
 
4.2%
2.5 13
 
5.4%
ValueCountFrequency (%)
15 1
 
0.4%
13 1
 
0.4%
11 3
 
1.2%
10 3
 
1.2%
9 6
 
2.5%
8 6
 
2.5%
7 10
4.2%
6 13
5.4%
5 15
6.2%
4.5 9
3.8%

Trans Fat (g)
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3141667
Minimum0
Maximum9
Zeros33
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:47.716401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.5
Q32
95-th percentile4.525
Maximum9
Range9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.6452185
Coefficient of variation (CV)1.2519101
Kurtosis2.8744789
Mean1.3141667
Median Absolute Deviation (MAD)0.5
Skewness1.6817877
Sum315.4
Variance2.7067441
MonotonicityNot monotonic
2025-01-19T19:54:47.756349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.1 36
15.0%
0 33
13.8%
0.2 22
9.2%
1 21
8.8%
2 20
8.3%
0.5 19
7.9%
1.5 16
 
6.7%
0.4 11
 
4.6%
3.5 11
 
4.6%
2.5 10
 
4.2%
Other values (8) 41
17.1%
ValueCountFrequency (%)
0 33
13.8%
0.1 36
15.0%
0.2 22
9.2%
0.3 10
 
4.2%
0.4 11
 
4.6%
0.5 19
7.9%
1 21
8.8%
1.5 16
6.7%
2 20
8.3%
2.5 10
 
4.2%
ValueCountFrequency (%)
9 1
 
0.4%
7 2
 
0.8%
6 5
 
2.1%
5 4
 
1.7%
4.5 6
 
2.5%
4 3
 
1.2%
3.5 11
4.6%
3 10
4.2%
2.5 10
4.2%
2 20
8.3%

Saturated Fat (g)
Categorical

High correlation 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0.0
178 
0.1
37 
0.2
21 
0.3
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters720
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 178
74.2%
0.1 37
 
15.4%
0.2 21
 
8.8%
0.3 4
 
1.7%

Length

2025-01-19T19:54:47.795747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:54:47.830311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 178
74.2%
0.1 37
 
15.4%
0.2 21
 
8.8%
0.3 4
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 418
58.1%
. 240
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 480
66.7%
Other Punctuation 240
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 418
87.1%
1 37
 
7.7%
2 21
 
4.4%
3 4
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 418
58.1%
. 240
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 418
58.1%
. 240
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Sodium (mg)
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3958333
Minimum0
Maximum40
Zeros111
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:47.864995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile25
Maximum40
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.6560004
Coefficient of variation (CV)1.3533812
Kurtosis2.4651044
Mean6.3958333
Median Absolute Deviation (MAD)5
Skewness1.6702051
Sum1535
Variance74.926342
MonotonicityNot monotonic
2025-01-19T19:54:47.903682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 111
46.2%
5 56
23.3%
10 28
 
11.7%
15 19
 
7.9%
25 9
 
3.8%
20 8
 
3.3%
35 5
 
2.1%
30 3
 
1.2%
40 1
 
0.4%
ValueCountFrequency (%)
0 111
46.2%
5 56
23.3%
10 28
 
11.7%
15 19
 
7.9%
20 8
 
3.3%
25 9
 
3.8%
30 3
 
1.2%
35 5
 
2.1%
40 1
 
0.4%
ValueCountFrequency (%)
40 1
 
0.4%
35 5
 
2.1%
30 3
 
1.2%
25 9
 
3.8%
20 8
 
3.3%
15 19
 
7.9%
10 28
 
11.7%
5 56
23.3%
0 111
46.2%

Total Carbohydrates (g)
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.8125
Minimum0
Maximum340
Zeros11
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:47.952398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.85
Q170
median125
Q3170
95-th percentile290.5
Maximum340
Range340
Interquartile range (IQR)100

Descriptive statistics

Standard deviation81.99981
Coefficient of variation (CV)0.63658271
Kurtosis-0.22489608
Mean128.8125
Median Absolute Deviation (MAD)55
Skewness0.48487636
Sum30915
Variance6723.9689
MonotonicityNot monotonic
2025-01-19T19:54:48.005434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 16
 
6.7%
125 11
 
4.6%
150 11
 
4.6%
0 11
 
4.6%
140 10
 
4.2%
80 9
 
3.8%
220 9
 
3.8%
120 8
 
3.3%
70 8
 
3.3%
170 8
 
3.3%
Other values (41) 139
57.9%
ValueCountFrequency (%)
0 11
4.6%
1 1
 
0.4%
4 1
 
0.4%
5 4
 
1.7%
10 5
2.1%
15 3
 
1.2%
20 2
 
0.8%
25 2
 
0.8%
30 2
 
0.8%
35 1
 
0.4%
ValueCountFrequency (%)
340 2
 
0.8%
330 2
 
0.8%
320 1
 
0.4%
310 1
 
0.4%
300 6
2.5%
290 4
1.7%
280 1
 
0.4%
270 2
 
0.8%
260 2
 
0.8%
250 3
1.2%

Cholesterol (mg)
Real number (ℝ)

High correlation  Zeros 

Distinct75
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.9375
Minimum0
Maximum90
Zeros8
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.057247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.95
Q121
median34
Q350.25
95-th percentile73.1
Maximum90
Range90
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation20.752785
Coefficient of variation (CV)0.5774688
Kurtosis-0.35934102
Mean35.9375
Median Absolute Deviation (MAD)14.5
Skewness0.39128579
Sum8625
Variance430.67809
MonotonicityNot monotonic
2025-01-19T19:54:48.105048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 10
 
4.2%
0 8
 
3.3%
23 8
 
3.3%
53 8
 
3.3%
42 7
 
2.9%
34 7
 
2.9%
16 7
 
2.9%
21 7
 
2.9%
37 7
 
2.9%
70 6
 
2.5%
Other values (65) 165
68.8%
ValueCountFrequency (%)
0 8
3.3%
1 2
 
0.8%
2 2
 
0.8%
3 1
 
0.4%
4 2
 
0.8%
6 1
 
0.4%
7 1
 
0.4%
8 2
 
0.8%
9 4
1.7%
10 3
 
1.2%
ValueCountFrequency (%)
90 2
 
0.8%
89 1
 
0.4%
88 1
 
0.4%
80 2
 
0.8%
78 4
1.7%
77 1
 
0.4%
75 1
 
0.4%
73 1
 
0.4%
72 1
 
0.4%
70 6
2.5%

Dietary Fibre (g)
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80833333
Minimum0
Maximum8
Zeros140
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.144024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.05
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4509892
Coefficient of variation (CV)1.7950382
Kurtosis9.2710846
Mean0.80833333
Median Absolute Deviation (MAD)0
Skewness2.8825081
Sum194
Variance2.1053696
MonotonicityNot monotonic
2025-01-19T19:54:48.183057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 140
58.3%
1 59
24.6%
2 25
 
10.4%
7 5
 
2.1%
3 4
 
1.7%
4 3
 
1.2%
6 3
 
1.2%
8 1
 
0.4%
ValueCountFrequency (%)
0 140
58.3%
1 59
24.6%
2 25
 
10.4%
3 4
 
1.7%
4 3
 
1.2%
6 3
 
1.2%
7 5
 
2.1%
8 1
 
0.4%
ValueCountFrequency (%)
8 1
 
0.4%
7 5
 
2.1%
6 3
 
1.2%
4 3
 
1.2%
3 4
 
1.7%
2 25
 
10.4%
1 59
24.6%
0 140
58.3%

Sugars (g)
Real number (ℝ)

High correlation  Zeros 

Distinct70
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.895833
Minimum0
Maximum84
Zeros14
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.342021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median32
Q343.25
95-th percentile71.1
Maximum84
Range84
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation19.686709
Coefficient of variation (CV)0.598456
Kurtosis-0.18194874
Mean32.895833
Median Absolute Deviation (MAD)12
Skewness0.47085281
Sum7895
Variance387.56651
MonotonicityNot monotonic
2025-01-19T19:54:48.390547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
5.8%
32 10
 
4.2%
23 9
 
3.8%
41 8
 
3.3%
33 7
 
2.9%
44 6
 
2.5%
38 6
 
2.5%
25 6
 
2.5%
17 6
 
2.5%
31 6
 
2.5%
Other values (60) 162
67.5%
ValueCountFrequency (%)
0 14
5.8%
3 1
 
0.4%
4 2
 
0.8%
5 1
 
0.4%
6 1
 
0.4%
7 3
 
1.2%
8 4
 
1.7%
9 2
 
0.8%
10 2
 
0.8%
11 2
 
0.8%
ValueCountFrequency (%)
84 2
0.8%
83 1
 
0.4%
80 1
 
0.4%
77 2
0.8%
76 2
0.8%
74 2
0.8%
73 2
0.8%
71 2
0.8%
69 3
1.2%
68 1
 
0.4%

Protein (g)
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0075
Minimum0
Maximum20
Zeros11
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.433429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q13
median6
Q310
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8796248
Coefficient of variation (CV)0.69634318
Kurtosis-0.24591286
Mean7.0075
Median Absolute Deviation (MAD)3
Skewness0.69673827
Sum1681.8
Variance23.810738
MonotonicityNot monotonic
2025-01-19T19:54:48.478500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 30
12.5%
6 25
 
10.4%
4 22
 
9.2%
5 22
 
9.2%
7 20
 
8.3%
10 13
 
5.4%
9 13
 
5.4%
0 11
 
4.6%
8 8
 
3.3%
16 8
 
3.3%
Other values (16) 68
28.3%
ValueCountFrequency (%)
0 11
 
4.6%
0.1 3
 
1.2%
0.2 1
 
0.4%
0.3 2
 
0.8%
0.4 3
 
1.2%
0.5 1
 
0.4%
1 6
 
2.5%
2 7
 
2.9%
3 30
12.5%
4 22
9.2%
ValueCountFrequency (%)
20 2
 
0.8%
19 3
 
1.2%
18 2
 
0.8%
17 4
1.7%
16 8
3.3%
15 7
2.9%
14 6
2.5%
13 7
2.9%
12 7
2.9%
11 7
2.9%

Vitamin A (% DV)
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8791667
Minimum0
Maximum50
Zeros27
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.518714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q315
95-th percentile25
Maximum50
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1120541
Coefficient of variation (CV)0.82112737
Kurtosis6.0920249
Mean9.8791667
Median Absolute Deviation (MAD)4
Skewness1.8479314
Sum2371
Variance65.805422
MonotonicityNot monotonic
2025-01-19T19:54:48.558997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 43
17.9%
4 37
15.4%
6 36
15.0%
15 36
15.0%
0 27
11.2%
8 23
9.6%
20 18
7.5%
25 11
 
4.6%
2 4
 
1.7%
50 3
 
1.2%
ValueCountFrequency (%)
0 27
11.2%
2 4
 
1.7%
4 37
15.4%
6 36
15.0%
8 23
9.6%
10 43
17.9%
15 36
15.0%
20 18
7.5%
25 11
 
4.6%
30 2
 
0.8%
ValueCountFrequency (%)
50 3
 
1.2%
30 2
 
0.8%
25 11
 
4.6%
20 18
7.5%
15 36
15.0%
10 43
17.9%
8 23
9.6%
6 36
15.0%
4 37
15.4%
2 4
 
1.7%

Vitamin C (% DV)
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6458333
Minimum0
Maximum100
Zeros187
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.596549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.477351
Coefficient of variation (CV)3.9709307
Kurtosis32.219169
Mean3.6458333
Median Absolute Deviation (MAD)0
Skewness5.6161921
Sum875
Variance209.59371
MonotonicityNot monotonic
2025-01-19T19:54:48.633728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 187
77.9%
2 20
 
8.3%
6 7
 
2.9%
15 7
 
2.9%
10 4
 
1.7%
20 4
 
1.7%
4 3
 
1.2%
80 3
 
1.2%
100 3
 
1.2%
8 2
 
0.8%
ValueCountFrequency (%)
0 187
77.9%
2 20
 
8.3%
4 3
 
1.2%
6 7
 
2.9%
8 2
 
0.8%
10 4
 
1.7%
15 7
 
2.9%
20 4
 
1.7%
80 3
 
1.2%
100 3
 
1.2%
ValueCountFrequency (%)
100 3
 
1.2%
80 3
 
1.2%
20 4
 
1.7%
15 7
 
2.9%
10 4
 
1.7%
8 2
 
0.8%
6 7
 
2.9%
4 3
 
1.2%
2 20
 
8.3%
0 187
77.9%

Calcium (% DV)
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.820833
Minimum0
Maximum60
Zeros23
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.669425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median20
Q330
95-th percentile50
Maximum60
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.571609
Coefficient of variation (CV)0.69985713
Kurtosis-0.18153324
Mean20.820833
Median Absolute Deviation (MAD)10
Skewness0.65512634
Sum4997
Variance212.33178
MonotonicityNot monotonic
2025-01-19T19:54:48.707312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 51
21.2%
20 34
14.2%
15 24
10.0%
0 23
9.6%
30 21
8.8%
25 21
8.8%
35 17
 
7.1%
45 11
 
4.6%
40 9
 
3.8%
50 9
 
3.8%
Other values (4) 20
 
8.3%
ValueCountFrequency (%)
0 23
9.6%
2 4
 
1.7%
6 2
 
0.8%
8 9
 
3.8%
10 51
21.2%
15 24
10.0%
20 34
14.2%
25 21
8.8%
30 21
8.8%
35 17
 
7.1%
ValueCountFrequency (%)
60 5
 
2.1%
50 9
 
3.8%
45 11
 
4.6%
40 9
 
3.8%
35 17
 
7.1%
30 21
8.8%
25 21
8.8%
20 34
14.2%
15 24
10.0%
10 51
21.2%

Iron (% DV)
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4666667
Minimum0
Maximum50
Zeros107
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.744872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile30
Maximum50
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.517913
Coefficient of variation (CV)1.4086491
Kurtosis2.4266327
Mean7.4666667
Median Absolute Deviation (MAD)2
Skewness1.6676372
Sum1792
Variance110.6265
MonotonicityNot monotonic
2025-01-19T19:54:48.783444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 107
44.6%
2 20
 
8.3%
10 17
 
7.1%
8 16
 
6.7%
20 16
 
6.7%
6 15
 
6.2%
15 12
 
5.0%
4 11
 
4.6%
25 9
 
3.8%
30 9
 
3.8%
Other values (3) 8
 
3.3%
ValueCountFrequency (%)
0 107
44.6%
2 20
 
8.3%
4 11
 
4.6%
6 15
 
6.2%
8 16
 
6.7%
10 17
 
7.1%
15 12
 
5.0%
20 16
 
6.7%
25 9
 
3.8%
30 9
 
3.8%
ValueCountFrequency (%)
50 2
 
0.8%
40 3
 
1.2%
35 3
 
1.2%
30 9
3.8%
25 9
3.8%
20 16
6.7%
15 12
5.0%
10 17
7.1%
8 16
6.7%
6 15
6.2%

Caffeine (mg)
Real number (ℝ)

Missing  Zeros 

Distinct34
Distinct (%)15.6%
Missing22
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean89.931193
Minimum0
Maximum410
Zeros34
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-01-19T19:54:48.825677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151.25
median75
Q3143.75
95-th percentile175
Maximum410
Range410
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation64.589236
Coefficient of variation (CV)0.71820727
Kurtosis2.7187664
Mean89.931193
Median Absolute Deviation (MAD)50
Skewness0.88086931
Sum19605
Variance4171.7694
MonotonicityNot monotonic
2025-01-19T19:54:48.868036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
75 37
15.4%
150 34
14.2%
0 34
14.2%
70 14
 
5.8%
95 11
 
4.6%
110 9
 
3.8%
130 7
 
2.9%
120 6
 
2.5%
25 6
 
2.5%
90 4
 
1.7%
Other values (24) 56
23.3%
(Missing) 22
 
9.2%
ValueCountFrequency (%)
0 34
14.2%
10 3
 
1.2%
15 3
 
1.2%
20 3
 
1.2%
25 6
 
2.5%
30 3
 
1.2%
50 3
 
1.2%
55 3
 
1.2%
65 1
 
0.4%
70 14
5.8%
ValueCountFrequency (%)
410 1
 
0.4%
330 1
 
0.4%
300 1
 
0.4%
260 1
 
0.4%
235 1
 
0.4%
225 1
 
0.4%
180 3
1.2%
175 4
1.7%
170 3
1.2%
165 2
0.8%

Interactions

2025-01-19T19:54:46.735018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-19T19:54:40.728459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-01-19T19:54:46.292053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-19T19:54:46.707890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-01-19T19:54:48.903799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BeverageBeverage_categoryBeverage_prepCaffeine (mg)Calcium (% DV)CaloriesCholesterol (mg)Dietary Fibre (g)Iron (% DV)Protein (g)Saturated Fat (g)Sodium (mg)Sugars (g)Total Carbohydrates (g)Total Fat (g)Trans Fat (g)Vitamin A (% DV)Vitamin C (% DV)
Beverage1.0000.9290.3000.4560.4020.3440.4060.5580.2140.4340.0000.0000.3380.4070.0000.0000.4430.871
Beverage_category0.9291.0000.2530.4080.3130.2910.2940.3600.0560.3700.0000.1010.3060.3500.0570.0820.3180.489
Beverage_prep0.3000.2531.0000.3440.3180.2310.1720.1260.1770.2840.5390.3850.1860.3350.1720.1620.3000.000
Caffeine (mg)0.4560.4080.3441.0000.2010.058-0.036-0.045-0.0180.1350.0000.048-0.0320.1570.1330.1160.142-0.170
Calcium (% DV)0.4020.3130.3180.2011.0000.4550.2270.2860.2940.8850.3460.4460.1930.4110.6270.5480.9020.248
Calories0.3440.2910.2310.0580.4551.0000.9440.4470.4580.5450.2230.3540.9090.7790.5860.6060.4170.445
Cholesterol (mg)0.4060.2940.172-0.0360.2270.9441.0000.3740.4000.3380.0000.2260.9830.7410.3530.4130.2260.388
Dietary Fibre (g)0.5580.3600.126-0.0450.2860.4470.3741.0000.8680.4360.000-0.1370.2580.2810.4950.4160.1920.226
Iron (% DV)0.2140.0560.177-0.0180.2940.4580.4000.8681.0000.3470.076-0.1740.3150.3360.5230.4220.1670.116
Protein (g)0.4340.3700.2840.1350.8850.5450.3380.4360.3471.0000.3600.5340.2510.4430.6040.5880.8970.412
Saturated Fat (g)0.0000.0000.5390.0000.3460.2230.0000.0000.0760.3601.0000.8190.0750.2460.4950.5380.4280.000
Sodium (mg)0.0000.1010.3850.0480.4460.3540.226-0.137-0.1740.5340.8191.0000.2230.3300.4420.6060.5800.279
Sugars (g)0.3380.3060.186-0.0320.1930.9090.9830.2580.3150.2510.0750.2231.0000.7390.3030.3720.1870.344
Total Carbohydrates (g)0.4070.3500.3350.1570.4110.7790.7410.2810.3360.4430.2460.3300.7391.0000.4740.5380.3920.248
Total Fat (g)0.0000.0570.1720.1330.6270.5860.3530.4950.5230.6040.4950.4420.3030.4741.0000.9310.4850.272
Trans Fat (g)0.0000.0820.1620.1160.5480.6060.4130.4160.4220.5880.5380.6060.3720.5380.9311.0000.4860.261
Vitamin A (% DV)0.4430.3180.3000.1420.9020.4170.2260.1920.1670.8970.4280.5800.1870.3920.4850.4861.0000.270
Vitamin C (% DV)0.8710.4890.000-0.1700.2480.4450.3880.2260.1160.4120.0000.2790.3440.2480.2720.2610.2701.000

Missing values

2025-01-19T19:54:47.174626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-19T19:54:47.267245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Beverage_categoryBeverageBeverage_prepCaloriesTotal Fat (g)Trans Fat (g)Saturated Fat (g)Sodium (mg)Total Carbohydrates (g)Cholesterol (mg)Dietary Fibre (g)Sugars (g)Protein (g)Vitamin A (% DV)Vitamin C (% DV)Calcium (% DV)Iron (% DV)Caffeine (mg)
0CoffeeBrewed CoffeeShort30.10.00.0050000.30000.0175.0
1CoffeeBrewed CoffeeTall40.10.00.00100000.50000.0260.0
2CoffeeBrewed CoffeeGrande50.10.00.00100001.00000.0330.0
3CoffeeBrewed CoffeeVenti50.10.00.00100001.00020.0410.0
4Classic Espresso DrinksCaffè LatteShort Nonfat Milk700.10.10.057510096.0100200.075.0
5Classic Espresso DrinksCaffè Latte2 Milk1003.52.00.1158510096.0100200.075.0
6Classic Espresso DrinksCaffè LatteSoymilk702.50.40.00656145.060208.075.0
7Classic Espresso DrinksCaffè LatteTall Nonfat Milk1000.20.20.051201501410.0150300.075.0
8Classic Espresso DrinksCaffè Latte2 Milk1506.03.00.2251351501410.0150300.075.0
9Classic Espresso DrinksCaffè LatteSoymilk1104.50.50.0010510168.01003015.075.0
Beverage_categoryBeverageBeverage_prepCaloriesTotal Fat (g)Trans Fat (g)Saturated Fat (g)Sodium (mg)Total Carbohydrates (g)Cholesterol (mg)Dietary Fibre (g)Sugars (g)Protein (g)Vitamin A (% DV)Vitamin C (% DV)Calcium (% DV)Iron (% DV)Caffeine (mg)
230Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Soymilk1701.50.20.00135371353.046106.00.0
231Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Grande Nonfat Milk2300.20.10.00190530524.086154.00.0
232Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Whole Milk2604.02.00.110190530524.066154.00.0
233Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Soymilk2402.00.20.00180511493.046158.00.0
234Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Venti Nonfat Milk3100.20.10.05260700696.0108204.00.0
235Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Whole Milk3506.03.00.215260700686.088204.00.0
236Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Tall Nonfat Milk1700.10.10.00160390384.060100.00.0
237Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Whole Milk2003.52.00.110160390383.060100.00.0
238Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Soymilk1801.50.20.00160371353.040106.00.0
239Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Grande Nonfat Milk2400.10.10.05230560555.080150.00.0